Most of conventional image coding algorithms have reduced spatial redundancies for compressing data. In our previous works, we have proposed a novel coding algorithm focused on luminance-chrominance (L-C) redundancies. This paper presents an improved coding algorithm by using both spatial correlation and L-C correlation in an opponent color space. First of all, an input color image is divided into a luminance component and two chromatic components in the opponent color space. The chromatic components are divided into regions, and each region is averaged for reducing spatial redundancies. In order to reduce the L-C redundancies, a ratio of chrominance to luminance, we call C/L component, was newly introduced. Finally the spatially averaged C/L components in each region and wavelet-coded luminance data are transmitted as compact code. The color image can be restored without degrading its sharpness by the multiplication of transmitted averaged C/L components to decoded luminance data. Moreover, the proposed coding algorithm has an option to optimize the C/L components in S-CIELAB visual space. This paper discusses the performances of proposed coding algorithm with experimental results for natural full color test images.